After a recent study that suggested automated essay graders are as effective as their human counterparts in judging essay exams, "roboreaders" are receiving a new wave of publicity surrounding their possible inclusion in assessments and classrooms. But while developers of the technology are happy to have the attention, they insist the high profile has more to do with timing of policy changes such as the push to common standards than with any dramatic evolution in the essay-grading tools themselves. "What's changed is the claims people are willing to make about it. "I think, over time, a mixture of technologies will make this really good not only for scoring essays," but also for other assignments, said Mr. Cohen, the director of AIR's assessment program. "But we really need to be clear about the limits of the applications we are using today so we can get there." The study, underwritten by the Menlo Park, Calif.-based William and Flora Hewlett Foundation, is driven by the push to improve assessments related to the shift to the Common Core State Standards in English/language arts and math, and is based on the examination of essays written specifically for assessments.

The threat of killer robots may sound a little far-fetched but this latest'harmful robot' suggests we may have taken a step closer to this dystopian reality. Roboticist Alexander Reben from the University of Berkeley, California, has created a bot called "The First Law" that is capable of pricking a finger, but is programmed to choose not to every time if it means avoiding being switched off. Ultimately, it can decide whether or not to inflict pain to serve its own interest. The robot is named after the first law in a set of rules devised by sci-fi author Isaac Asimov, which - quoted as being from the Handbook of Robotics, 2058 AD – states "a robot may not injure a human being or, through inaction, allow a human being to come to harm". Reben's research paper explains how the robot operates in relation to "reinforcement learning agents" and how they are unlikely to behave optimally all the time.

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout.

"Hindsait's main goal has been to develop a robust, AI platform that specifically addresses the needs of healthcare organizations," said Frost & Sullivan Research Analyst Harpreet Singh Buttar. "This platform assists in reducing unnecessary health services, eliminating errors and biases in care delivery and improving overall quality of care." Hindsait's system has proven to be highly adaptable and scalable, based on unique use case requirements. Their capabilities range from natural language processing (NLP) and machine learning to cognitive computing and predictive analytics that directly helps providers and payers resolve healthcare delivery issues. Hindsait boasts a wide range of services, right from analyzing unstructured data, such as clinical notes, patient charts, and prescriptions, to real-time optimization of diagnostic and treatment plans.

The company said the new Edgeline EL1000 (pictured above) and Edgeline EL4000 converged systems target enterprise clients deploying IoT devices in remote environments, also known as the "edge of the network." While the recent proliferation of IoT devices has made it increasingly possible for companies to gather massive amounts of data from remote locations in the field, retrieving that information and performing intensive analytics on it has been challenging. Some companies, such as those in the oil and gas, manufacturing, and telecommunications industries face particular challenges when it comes to harnessing big data in remote environments, HPE said. "Until now, the remote data would have to be transported to a data center or cloud for analysis, which can be a slow, risky and inefficient process," the company said in a statement. The two new systems are aimed at enabling organizations to harness their data onsite by delivering real-time analytics and machine learning at the point of data collection.

WASHINGTON – Scientists have long been baffled by the smarts displayed by some birds with tiny brains. But a new explanation may turn the term "bird brain" on its head: Birds have more densely packed neurons in their brains than other animals, contributing to cognitive ability on par with that of primates, researchers said on Monday. A macaw's brain may be the size of a shelled walnut, far smaller than that of a macaque monkey -- which has a brain the size of a lemon -- but the parrot has many more neurons, or brain nerve cells, in its forebrain, a region crucial for intelligence, according to a study published in the Proceedings of the National Academy of Sciences. The researchers were the first to systematically measure neurons in the brains of 20 bird species ranging in size from the tiny finch to the six-foot (1.8-meter) emu. "For a long time having a'bird brain' was considered to be a bad thing," said senior author Suzana Herculano-Houzel, a neuroscientist at Vanderbilt University.

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

In a talk in Pittsburgh in 1997, the late evolutionary biologist Stephen J. Gould allegedly characterized humans as "the primates who tell stories." Psychologist Robyn Dawes went much further, suggesting humans are "the primates whose cognitive capacity shuts down in the absence of a story." To be sure, we love a good story. Research suggests that anecdotes can be as persuasive as hard data, and that jurors are influenced by the quality of the prosecution's and defense's "stories" when deciding whether to find a defendant guilty. Even in science, we seek explanations, not mere descriptions; in history, we want a good narrative, not a mere sequence of events. Or do they offer something more?

Date Science, or Machine Learning, is a scary topic. It's hard to know where to get started. It's hard to even find a good definition of what it does and what you have to do. As I've given a few ad hoc presentations on Machine Learning (and though focused on implementing it with Azure, the basics are applicable to other platforms) I thought I'd take my random notes and present them as a primer. You don't need to be a Rocket Scientist to get started, but having a basic understanding of Linear Algebra will be helpful.

Nuclear energy provides electricity for a large segment of the global population, but it has one Achilles' heel that remains to be adequately addressed: the waste. According to a report from Business Standard, however, scientists from the DOE/Lawrence Berkeley National Laboratory have made a breakthrough finding that could lead to a viable solution to this lingering problem. A team of scientists has developed a new material that can clean up nuclear waste gases created as a byproduct of fuel reprocessing plants. The researchers say the material allows for the efficient, safe and cheap disposal of harmful byproducts of generating nuclear energy. The study was carried out by an international team based in Switzerland at the Ecole Polytechnique Federale de Lausanne (EPFL).